markus41/redis

Redis Skill

Provides comprehensive Redis capabilities for the Golden Armada AI Agent Fleet Platform.

When to Use This Skill

Activate this skill when working with:

• Caching implementation
• Session management
• Pub/Sub messaging
• Rate limiting
• Distributed locks

Redis CLI Quick Reference

Connection

```bash

Connect

redis-cli -h localhost -p 6379 redis-cli -h localhost -p 6379 -a password

Test connection

redis-cli ping ```

Basic Operations

```bash

Strings

SET key "value" SET key "value" EX 3600 # With TTL GET key DEL key EXISTS key TTL key EXPIRE key 3600

Hashes

HSET user:1 name "John" age "30" HGET user:1 name HGETALL user:1 HDEL user:1 age

Lists

LPUSH queue "item1" RPUSH queue "item2" LPOP queue RPOP queue LRANGE queue 0 -1

Sets

SADD tags "python" "redis" SMEMBERS tags SISMEMBER tags "python" SREM tags "python"

Sorted Sets

ZADD leaderboard 100 "player1" 200 "player2" ZRANGE leaderboard 0 -1 WITHSCORES ZRANK leaderboard "player1" ZINCRBY leaderboard 50 "player1" ```

Python Redis Client

```python import redis from redis import asyncio as aioredis

Synchronous client

r = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)

Async client

async_redis = aioredis.from_url("redis://localhost:6379", decode_responses=True)

Basic operations

r.set('key', 'value', ex=3600) value = r.get('key') r.delete('key')

Hash operations

r.hset('user:1', mapping={'name': 'John', 'age': '30'}) user = r.hgetall('user:1')

List operations

r.lpush('queue', 'item1', 'item2') items = r.lrange('queue', 0, -1) item = r.rpop('queue')

Async operations

async def cache_get(key: str): async with aioredis.from_url("redis://localhost") as redis: return await redis.get(key) ```

Caching Patterns

Cache-Aside Pattern

```python async def get_agent(agent_id: str) -> dict: # Try cache first cache_key = f"agent:{agent_id}" cached = await redis.get(cache_key) if cached: return json.loads(cached)

# Cache miss - fetch from database
agent = await db.get_agent(agent_id)
if agent:
    await redis.set(cache_key, json.dumps(agent), ex=3600)

return agent

async def update_agent(agent_id: str, data: dict) -> dict: # Update database agent = await db.update_agent(agent_id, data)

# Invalidate cache
cache_key = f"agent:{agent_id}"
await redis.delete(cache_key)

return agent

```

Rate Limiting

```python async def rate_limit(key: str, limit: int, window: int) -> bool: """ Sliding window rate limiter. Returns True if request is allowed. """ current = int(time.time()) window_key = f"rate:{key}:{current // window}"

async with redis.pipeline() as pipe:
    pipe.incr(window_key)
    pipe.expire(window_key, window * 2)
    results = await pipe.execute()

count = results[0]
return count <= limit

Usage

if not await rate_limit(f"user:{user_id}", limit=100, window=60): raise HTTPException(status_code=429, detail="Rate limit exceeded") ```

Distributed Lock

```python import uuid

class DistributedLock: def init(self, redis_client, key: str, timeout: int = 10): self.redis = redis_client self.key = f"lock:{key}" self.timeout = timeout self.token = str(uuid.uuid4())

async def __aenter__(self):
    while True:
        acquired = await self.redis.set(
            self.key, self.token, nx=True, ex=self.timeout
        )
        if acquired:
            return self
        await asyncio.sleep(0.1)

async def __aexit__(self, exc_type, exc_val, exc_tb):
    # Only release if we own the lock
    script = """
    if redis.call("get", KEYS[1]) == ARGV[1] then
        return redis.call("del", KEYS[1])
    else
        return 0
    end
    """
    await self.redis.eval(script, 1, self.key, self.token)

Usage

async with DistributedLock(redis, "resource:123"): await do_critical_work() ```

Pub/Sub

```python

Publisher

async def publish_event(channel: str, message: dict): await redis.publish(channel, json.dumps(message))

Subscriber

async def subscribe_events(): pubsub = redis.pubsub() await pubsub.subscribe("agent:events")

async for message in pubsub.listen():
    if message["type"] == "message":
        data = json.loads(message["data"])
        await handle_event(data)

```

Session Management

```python from fastapi import Depends, HTTPException from fastapi.security import HTTPBearer import secrets

security = HTTPBearer()

async def create_session(user_id: str) -> str: session_id = secrets.token_urlsafe(32) session_key = f"session:{session_id}"

await redis.hset(session_key, mapping={
    "user_id": user_id,
    "created_at": datetime.utcnow().isoformat()
})
await redis.expire(session_key, 86400)  # 24 hours

return session_id

async def get_session(token: str = Depends(security)) -> dict: session_key = f"session:{token.credentials}" session = await redis.hgetall(session_key)

if not session:
    raise HTTPException(status_code=401, detail="Invalid session")

# Refresh TTL
await redis.expire(session_key, 86400)
return session

```

Best Practices

1. Use connection pooling for production
2. Set TTL on all keys to prevent memory bloat
3. Use pipelining for batch operations
4. Implement proper error handling for connection issues
5. Monitor memory usage with INFO memory
6. Use Lua scripts for atomic operations